Research on the Adaptation Strategies of Ergonomically-Based Textile Soft Furnishings in Health Care Space Design

Smart textile systems integrated with intelligent sensing and adaptive optimization technologies have emerged as an important component of next-generation healthcare environments. This study proposes an Adaptive Ergonomic– Smart Textile Optimization Framework (AESTOF) for healthcare space applications by integrating ergonomicbiomechanical modeling, smart textile functional characterization, environmental comfort analysis, and multi-objective decision optimization. The framework combines anthropometric mapping, pressure distribution simulation, thermalacoustic performance evaluation, antimicrobial efficiency assessment, and IoT-based sensor feedback to establish a quantitative adaptive textile evaluation system. A hybrid AHP–TOPSIS decision model is further employed to optimize textile configuration and adaptive performance. Simulation results demonstrate that the proposed framework reduces peak interface pressure by 34.6%, improves thermal comfort stability by 27.3%, decreases ergonomic risk by 22.8%, and enhances antimicrobial efficiency by 18.5% compared with conventional furnishing solutions. The results verify the effectiveness of intelligent sensing, adaptive control, and data-driven optimization in healthcare textile systems. This work provides a practical engineering framework for smart textile applications, intelligent sensing environments, and human-centered adaptive healthcare infrastructure.